class_docstr: An implementation of `"LDP" <https://arxiv.org/abs/1811.03508>`_ from the
  ICLR Representation Learning on Graphs and Manifolds Workshop '19 paper "A
  Simple Yet Effective Baseline for Non-Attributed Graph Classification". The
  procedure calculates histograms of degree profiles. These concatenated
  histograms form the graph representations.
  
  Args:
      bins (int): Number of histogram bins. Default is 32.
docstr: Getting the embedding of graphs.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of graphs.
docstr_tok: ["Getting","the","embedding","of","graphs",".","Return","types",":","*","*","*","embedding","*","*","*","(","Numpy","array",")","*","-","The","embedding","of","graphs","."]
code: def get_embedding(self) -> np.array:
      r"""Getting the embedding of graphs.
  
      Return types:
          * **embedding** *(Numpy array)* - The embedding of graphs.
      """
      return np.array(self._embedding)
  
file_path: karateclub/karateclub/graph_embedding/ldp.py
imports: import numpy
  import networkx
  from karateclub.estimator import Estimator
  from typing import List
label_desc: Use image node_id 4 for calling the LDP obj's underlying member method code with example usage: obj.get_embedding() and returns: np